1. Field of the Invention
The present invention generally relates to noise reduction methods and image decoding devices, and particularly relates to a method of reducing quantization noise generated during a decoding process of coded image data and to a device for decoding the coded image data based on the method.
2. Description of the Related Art
When transmitting, recording, or reproducing various signals such as video signals, audio signals, etc., as digital signals, techniques for compressing and decompressing information are generally employed to reduce the amount of information, i.e., the number of bits. For example, if a linear quantization (uniform quantization) which represents each sample value with a value selected from evenly divided signal levels is used for digitizing video signals, audio signals, and the like without any compression technique, the amount of information to be transmitted or recorded/reproduced becomes prohibitively large.
Thus, in the fields of broadcasting, communication, and information recording/reproducing, characteristics of human visual perception and auditory perception have been utilized in such compression techniques. For example, the human perception is sensitive to changes in signal levels when a signal has a little variation, while it is not so sensitive to the changes when a signal has a strong fluctuation. Such characteristics can be utilized to reduce the amount of information for each sample value. Also, a number of technologies for compressing information have been employed to bring about an advance in the practical use of highly efficient compression techniques.
For example, the amount of information contained in a one-hour moving picture having an image quality similar to those reproduced by VHS-type VTR devices is about 109 Gbits. Also, 360 Gbits more or less are contained in such a one-hour moving picture with an image quality comparable to reception images of NTSC color-television sets. Thus, an effort to develop highly efficient compression techniques is also directed to application studies aimed at transmitting or recording/reproducing such a large amount of information by means of current transmission lines or recording media.
Highly efficient compression methods which have been proposed as practical methods for image-information application typically combine three different compression techniques to reduce the amount of information. The first technique reduces the amount of information by utilizing correlation within an image frame (compression utilizing spatial correlation), which takes advantage of the fact that there is high correlation between adjacent pixels in natural images. The second technique reduces the amount of information by utilizing correlation between image frames arranged in time (compression utilizing temporal correlation). The third technique reduces the amount of information by utilizing a different probability of appearance of each code.
As techniques for compressing image information by utilizing correlation within an image frame (the first technique), a variety of techniques have been proposed. In recent years, orthogonal transformation such as the K-L (Karhunen-Loeve) transform, the Discrete Cosine Transform (DCT), the discrete Fourier transform, and the Walsh-Hadamard transform have often been employed.
For example, highly efficient coding methods for image information proposed by the MPEG (moving picture coding expert group) which has been established under the ISO (international standardization organization) employ two-dimensional DCT. These highly efficient coding methods (MPEG1 and MPEG2) combine intra-frame coding and inter-frame coding to realize highly efficient coding of moving-picture information while employing motion compensation prediction and inter-frame prediction. The orthogonal transformation is generally applied to blocks which are generated by dividing an image into unit blocks having a predetermined block size (M.times.N). In MPEG1 and MPEG2, a block having an 8-pixel-by-8-pixel block size is defined as a unit block.
M.times.N orthogonal transform coefficients which are obtained by orthogonally transforming the unit block (e.g., 64 DCT transform coefficients in MPEG1 and MPEG2) are then quantized by using block-quantization step sizes (intervals for quantization). The block-quantizatoin step sizes are defined for each predetermined-size area including at least one unit block. In MPEG1 and MPEG2, for example, this predetermined-size area is called a macro block, which consists of a block of 16.times.16 pixels for a luminance signal Y and a block of 8.times.8 pixels for each of color signals Cr and Cb. In detail, the block-quantization step sizes are represented as {a quantization characteristic of a macro block (a quantization scale of a macro block) QS} x quantization matrix (8.times.8)!. Here, the quantization characteristic of a macro block changes from macro block to macro block.
The orthogonal transform coefficients (e.g., DCT coefficients) which are quantized based on the block-quantization step sizes are separated into a direct current component (DC component) and alternating current components (AC components). The direct current component of the orthogonal transform coefficients is subjected to differential coding, and the alternating current components of the orthogonal transform coefficients are subjected to entropy coding after a zigzag scan. Here, the entropy coding is an information compression technique using a variable-length coding scheme which utilizes a different probability of appearance of each code such as in the Huffman coding.
Transformed and coded image data is transmitted as a bit stream (a series of bits). A decoding operation on the transformed and coded image data is carried out in a reversed manner to the coding operation described above so as to generate an output image. However, when the quantization process is included in the entire coding process, unavoidable quantization errors result in quantization noise appearing in the output image. Thus, when the complexity of an image subjected to the coding process contains a larger amount of information than capacity of a transmission rate, the quantization noise will substantially degrade image quality.
In general, the quantization errors in low-frequency components result in block distortions in the output image, by which there appears to be no correlation between each block of the output image. Also, the quantization errors in high-frequency components generate mosquito noise around edges, which is a distortion having a ringing appearance in the output image.
The quantization errors appearing in the output image are especially conspicuous where image levels are generally flat. When a small amount of the quantization noise is added at a point where a change in a video signal level has frequency components from low frequencies to high frequencies, the noise is difficult to be visually detected because of characteristics of visual perception. However, when a small amount of noise having high frequency components is added at a point where a change in the video signal has only low frequency components, the noise is easy to detect. Of course, when a large amount of noise is added, the noise is detected as coding degradation irrespective of the frequency components of the noise.
In order to obviate the problem of an image-quality degradation caused by the quantization noise, Japanese Laid-Open Patent Application No.4-2275 discloses a technique for reducing the quantization errors in low frequency components which appear as block distortion in an image. The technique detects an activity value (the amount of high frequency components contained in a given block of image data) for a given block. Based on the activity value, pixels near the borders of the given block are processed by a low-pass filter, and, then, random noise is added to these pixels processed by the low-pass filter.
However, this technique has a problem when the image degradation is substantial as in the case where an image to be subjected to highly efficient coding is relatively complex in comparison with a transmission rate. In such a case, the quantization noise causing the image degradation is mistakenly detected as part of the activity value of the decoded image. Thus, even if a block is of a low activity value, this block might be mistakenly judged as having a high activity value. That is, it is difficult to determine whether the activity value detected in the decoded image reflects a real activity value inherent in the image data or reflects the quantization noise sneaking into the image data. As a result, the technique described above cannot effectively reduce the block distortion generated during the decoding process of the coded image data.
Accordingly, there is a need for a method which can effectively reduce the quantization noise generated during a decoding process of coded image data, and for a device for decoding the coded image data based on this method.